The CQT representation of an audio signal depends on the number of octaves of frequencies and the number of frequency bins per each octave. The number of octaves depends upon the chosen minimum frequency (\(F_{\mathrm {min}}\) ) and the maximum frequency (\(F_{\mathrm {max}}\) ) of operation, and this equals to \( \log _{2}{\frac{F_{\mathrm {max}}}{F_{\mathrm {min}}}}\)  [1]}. The CQT representation with reduced number of total frequency bins over a fixed number of octaves will provide detailed information for lower frequency region with reduced redundancy. Conversely, due to linearly spaced frequency bins, short-time Fourier transform (STFT) does not offer this flexibility. Fixing \(F_{\mathrm {min}}\)  to \(32.7\)  Hz and \(F_{\mathrm {max}}\)  to Nyquist frequency gives us approximately 8 octaves. Hop length in CQT computation defines the number of time samples by which CQT computation window moves. The CQT also has resemblance with continuous wavelet transform (CWT) which provides variable time-frequency resolution and has been found helpful for SER [2]}.
